The Computational Mechanisms of Detached Mindfulness

Abstract

This paper investigates the computational mechanisms underlying a type of metacognitive monitoring known as detached mindfulness, a particularly effective therapeutic technique within cognitive psychology. While research strongly supports the capacity of detached mindfulness to reduce depression and anxiety, its cognitive and computational underpinnings remain largely unexplained. We employ a computational model of metacognitive skill to articulate the mechanisms through which a detached perception of affect reduces emotional reactivity.

Full text

The Computational Mechanisms of Detached Mindfulness
Brendan Conway-Smith (brendan.conwaysmith@carleton.ca),
Robert L. West (robert.west@carleton.ca)
Department of Cognitive Science, Carleton University, Ottawa, ON K1S 5B6 Canada
Abstract
This paper investigates the computational mechanisms
underlying a type of metacognitive monitoring known as
detached mindfulness, a particularly effective therapeutic
technique within cognitive psychology. While research
strongly supports the capacity of detached mindfulness
to reduce depression and anxiety, its cognitive and
comp ut a t iona l u nd e rpinn in g s rema in l a rge ly
unexplained. We employ a computational model of
metacognitive skill to articulate the mechanisms through
which a detached perception of affect reduces emotional
reactivity.
Keywords: metacognition; mindfulness; affect; emotion;
proceduralization; ACT-R; Common Model
Introduction
The attempt to build a Unified Cognitive Architecture
(N ew ell, 1994) that c an replic ate h uman-like
intelligence must necessarily account for the routine
interplay between affect and metacognitive processes.
Historically, cognitive modeling research has focused
predominantly on knowledge-based processing such as
reasoning, vision, and AI problem-solving, with little or
no computational account of the critical role of emotion
and metacognition.
This need for increased computational understanding
is underscored by the fact that perseverative patterns of
negative emotion, such as depression and anxiety, are
the largest causes of cognitive disability worldwide
(World Health Organization, 2022). Consequently, there
has been a global push to develop metacognitive
techniques that allow individuals to engage with their
em o tio ns ada p tiv ely. A part icu lar l y e ff e cti ve
metacognitive technique is referred to as ‘detached
mindfulness’ (Wells, 2005). This technique focuses on
developing one’s perception of the momentary changes
of affective states, shown to significantly reduce feelings
of distress, emotional reactivity, and to improve overall
cognitive functioning (Hammersmark et al., 2024).
While decades of clinical research strongly supports
the effectiveness of metacognitive strategies and
detached mindfulness in particular, their underlying
cognitive and computational mechanisms remain
largely unexplained. This paper will investigate the
cognitive and computational constituents that underpin
detached mindfulness and its therapeutic benefits.
Specifically, we will discuss the metacognitive
mechanisms by which the perception of affective
fluctuations deactivates emotional reactivity.
For this purpose, we will employ the Common Model
of Cognition ( CM C) , originally the ‘Standard
Model’ (Laird, Lebiere, & Rosenbloom, 2017), which
provides a unified framework for investigating the
fundamental elements of cognitive and metacognitive
phenomena. By utilizing the Common Model, and
specifically ACT-R (Anderson & Lebiere, 1998) in this
investigation, we intend to address important questions
largely unexplored in cognitive models: How does
metacognitive training in detached mindfulness reduce
persevering styles of negative emotions? By what
comput at io nal mecha ni sm does p er ce iving the
momentary changes in affect disengage emotional
reactivity such as meta-emotions?
First, we will overview the relevant literature on
metacognition and mindfulness techniques. Second, we
outline the computational mechanisms involved in a
model of metacognitive skill learning. Third, we apply
this model of metacognitive skill learning to detached
mindfulness to clarify its underling components and the
precise mechanism by which it reduces emotional
reactivity as reported in the literature.
Metacognition
We propose that an active mechanism of detached
mindfulness fundamentally relies on a form of
automatized metacognition. The common conception of
metacognition refers to the monitoring and control of
cognitive processes (Flavell 1979; Fleming, Dolan, &
Frith, 2012). Metacognitive control refers to the active
regulation of cognitive processes or states to either
activate or inhibit them (Proust, 2013; Wells, 2019).
The regulation of one’s own cognitive processes can
involve various processes such as attention, emotion,
planning, reasoning, and memory (Efklides, Schwartz,
& Brown, 2017; Pearman et al., 2020). Metacognitive
monitoring refers to the capacity to recognize and
identify cognitive states. It involves the perception of
internal mental states such as thoughts and feelings in
order to regulate those states or direct behavior.
Studies demonstrate that metacognitive monitoring
can be developed and improved through training (Baird,
Mrazek, Phillips, & Schooler, 2014). For instance,
attentional processes can be developed and enhanced
through the repeated practice of attention-based tasks
(Posner et al., 2015). Metacognitive training such as
mindfulness techniques is integral to both Cognitive
Be hav ior Ther apy (CBT; Dob son, 201 3) and

Metacognitive Therapy (MCT; Normann & Morina,
2018) and facilitates improved control over maladaptive
thoughts and emotions (Wells, 2011, 2019; Hagen et al.,
2017). The benefits of mindfulness training rely partly
on its enhancement of metacognitive sensitivity, which
is the extent to which one is able to perceive their own
mental processes or states, including thoughts, feelings,
and emotions (Fleming & Lau, 2014). Improved
metacognitive sensitivity has the effect of lowering
one’s metacognitive threshold — the minimal level of a
stimulus required for a person to be aware of some
mental state and make a judgment about it (Charles,
Chardin, & Haggard, 2020; Pauen & Haynes, 2021).
The metacognitive threshold can also be lowered by
way of a ttentional training, such as detac hed
mindfulness and meditation, which allows one to
perceive a weaker signal strength from internal
cognitive states (Fox et al., 2016). While this has been
effectively modelled within ACT-R (Conway-Smith &
West, 2023) it is not the main focus of this paper.
Metacognition as mindfulness
Scientific interest in mindfulness practice has become a
target of interdisciplinary research and has grown
exponentially over the past few decades (Van Dam et
al., 2018). Metacognition and mindfulness are often
used interchangeably within cognitive psychology
(Holas & Jankowski, 2013). Mindfulness psychology
contends that a significant degree of emotional distress
and pathological symptoms are caused by the illusory
perce ption of a ffective experie nce being more
permanent than it actually is. This perceptual illusion
has been explained as the result of poor metacognitive
sensitivity that obscures the detection of affective
fluctuation (Brown & Ryan, 2003; Grossman et al.,
2010). To address this metacognitive deficiency,
detached mindfulness has emerged as a uniquely
effective therapeutic technique (Wells & Matthews,
1994; Hammersmark et al., 2024). This involves
participants learning to observe moment-to-moment
changes in mental states, including subtle emotional
fluctuations, and allowing these states to occur without
engaging with or reacting to them.
This non-reactive state of awareness is also referred
to as ‘equanimity’. In mindfulness therapies that do not
promote equanimity, awareness alone is ofte n
insufficient to increase subjects’ psychological well-
being (Cardaciotto et al., 2008). Detached mindfulness
is most closely aligned with Vipassana meditation (in
the tradition of S.N. Goenka), an old and popular
technique that largely focuses on cultivating equanimity
i.e., perceptual sensitivity to variations in affect and
physical sensation (Kakumanu et al., 2018). Regular
practice of this technique has shown to improve
executive functioning, response inhibition, and control
over emotional reactions such as meta-emotions
(Andreu et al., 2019).
Meta-emotion
Meta-emotions are emotions that automatically react to
other emotions (Jäger & Banninger-Huber, 2015;
Predatu, David & Maffei, 2020). For instance, a primary
negative emotion (sadness) can cause a greater
secondary negative emotion (despair) which may cause
an even greater tertiary negative emotion (depression).
Meta-emotions are instances of positive feedback, in
which an emotional response to a primary emotion
intensifies the overall emotional experience, leading to
an amplified response. Meta-emotions occur as low-
level reactive processes that are largely unconscious
and involuntary, making them difficult to intervene in.
While therapeutic practices aim to control the
resulting effects of meta-emotions such as anxiety and
depression, techniques such as detached mindfulness
and Vipassana aim to address the source, which is
con side red the fa l se perc epti on o f a ffe ctiv e
pe rmanenc e. To da te, w e l ack a mechan istic
understanding of precisely how detached mindfulness
breaks through the illusion of affective permanence and
disengages emotional reactivity. To clarify this
mechanism, we will apply a model of metacognitive
skill that articulates the components involved in this
process and how they interact. Central to this
e x pl a na t io n i s a p r oc e ss r e f e r r e d t o a s
proceduralization, a framework common among skill
theories. We will first discuss the relevant components
of metacognition and their expression in the cognitive
architecture ACT-R. We will then explore how the
components of proceduralization function to produce
the th er ap eu ti c mechanism active in detached
mindfulness.
Components of metacognition
There are at least two types of cognitive representations
that can engage in metacognitive monitoring and
control processes — declarative knowledge and
procedural knowledge. Metacognitive knowledge, or
meta-knowledge, is considered a form of declarative
knowledge (Schraw & Moshman, 1995; McCormick,
2003; Wells, 2019). Meta-knowledge takes the form of
an explicit metarepresentation that is propositionally
formatted and refers to a cognitive property, e.g., “I am
focused” (Shea et al., 2014; Proust, 2013). Meta-
knowledge can also take the form of a metacognitive
instruction, which specifies a mental action to be
performed (Wells, 2019). A metacognitive instruction,
or meta-instruction, prescribes an action directed
toward controlling some cognitive process, e.g., “Focus
on the current task.” Metacognitive knowledge is
considered to be distinct from metacognitive skill, as it
does not automatically lead to the deployment of
metacognitive processes (Veenman & Elshout, 1999).
The execution of metacognitive instructions is
pe rfo rme d b y w ay of pr oce dural kn owl edge.
Improvements in metacognition are said to involve the

refining of procedural knowledge that people use to
monitor and control their own cognitive processes
(Brown & DeLoache, 1978; Schraw & Moshman, 1995;
Wells, 2019). The various realms of metacognitive
skills can be understood as different domains of
procedural knowledge (Veenman et al., 2005).
ACT-R
Various theories of metacognition have been modelled
within the ACT-R cognitive architecture (Reitter, 2010;
Anderson & Fincham, 2014). ACT-R instantiates
decades of research on how human cognition functions
computationally. It s mandate i s to de pi ct the
components necessary for human intelligence, which
in clude w ork ing memor y, p erc ept ion , a cti on,
declarative memory, and procedural memory. These
modules have also been correlated with their associated
brain regions (Borst et al., 2015).
The ACT-R cognitive architecture fundamentally
distinguishes between declarative and procedural
knowledge, which accords with the literature on skill
acquisition in philosophy and psychology (Squire,
1992; Christensen, Sutton, & McIlwain, 2016).
Declarative knowledge is formatted propositionally and
structured within semantic networks. Procedural
knowledge is commonly referred to by researchers as
containing “procedural representations” (Anderson,
1982; Pavese, 2019). Within ACT-R, procedural
representations are computationally specified as
“production rules” which are a dominant form of
representation within accounts of skill (Newell, 1994;
Taatgen & Lee, 2003; Anderson et al., 2019).
Neurologically, production rules are associated with the
50ms decision timing in the basal ganglia (Stocco,
2018). Production rules, or “productions”, transform
information and change the state of the system to
complete a task or resolve a problem. A production rule
is modeled after a computer program instruction in the
form of a “condition-action” pairing. It specifies a
condition that, when met, performs a prescribed action.
A production is also thought of as an “if-then” rule.
If the condition is satisfied, such as matching to
working memory, then it fires an action (Figure 1).
Figure 1: Production rules are formatted as a condition-
action pairing. IF the condition side matches to the cue in
working memory, THEN it fires an action.
Affect have been modelled computationally within
ACT-R as non-propositional representations in working
memory, or “metadata” (West & Conway-Smith, 2019).
These types of affective information, encompassing
both emotional states and noetic feelings, are essentially
regarded as patterns within working memory that can
be accessed by production rules.
Production rules match to and fire off the content in
working memory. Should any stimuli or pattern appear
in working memory, productions that match this pattern
will arise from procedural memory and fire a prescribed
action. In this way, cues in working memory can
prompt procedural knowledge to act within various
domains — motor, cognitive, and metacognitive. It is
these specific cognitive units that are developed and
refined during the process of proceduralization.
Proceduralization
The concept of proceduralization is often used within
the skill acquisition literature to explain the cognitive
mechanisms involved in task learning (Fitts & Posner,
1967; Dreyfus & Dreyfus, 1986; Kim & Ritter, 2015).
It refers to the process by which a task becomes
automated, allowing it to be performed more
efficiently and accurately, with minimal conscious
effort or attention. The process involves converting
slow declarative knowledge into fast procedural
knowledge which is then increasingly refined. Skill
performance can be further improved by way of
mechanisms such as time delayed learning, where faster
productions are rewarded. Proceduralization plays a
significant role in the cognitive processes involved in
skill learning within domains such as motor skill,
cognitive skill, and metacognitive skill (Fitts, 1964;
Anderson, 1982).
Metacognitive proceduralization
Metacognitive proceduralization involves a mechanism
by which human cognition becomes more skillful at
monitoring and controlling its own processes, such as
attention, emotion, and metacognitive sensitivity
(Conway-Smith, West, & Mylopoulos, 2023). Previous
rese ar ch has pr es ented p ro ce duralization as a
mechanism that can lower the metacognitive threshold,
allowing one to perceive increasingly weaker signals
from mental states and more subtle changes in affect
(Conway-Smith & West, 2023). It is hypothesized that
proceduralization accomplishes this through the
building and refining of simpler, faster production rules.
Faster and less complex productions, particularly those
that notice internal states, increase the chances of
picking up fleeting or intermittent signals related to
emotions and epistemic feelings, such as confidence
and feelings of knowing (FoK). However, this model
does not address the process by which it mitigates
emotional reactivity. By extending this research on
metacognitive proceduralization, we can investigate a
mec hani sm wher eby suf fici ent met acog niti ve
sensitivity can be developed to deactivate meta-
emotions.

Metacognitive skill progresses through stages that
parallel those of motor skill and cognitive skill, from an
early stage of instruction-following to an expert stage
that relies on automatic procedural knowledge
(production rules).
Figure 2: Three stages of metacognitive skill learning
through the process of proceduralization (Conway-Smith,
West , & My lo po ul os , 20 23 ).
Metacognitive training in detached mindfulness
progresses through the following three stages (Figure 2):
The novice stage involves the use of written or verbal
meta-instructions to monitor or control some cognitive
state (such as attentional training or meditation). In the
case of metacognitive training in equanimity, meta-
instructions direct the novice’s attention toward the
momentary changes of affective experience (feeling,
sensation, or emotion). These meta-instructions are
carried out by productions that retrieve them from
declarat iv e memory an d execute the m. Initial
metacognitive performance is slow, effortful, error-
prone, and requiring a large degree of working
memory.
The intermediate stage of metacognitive training
involves proceduralization, where practicing meta-
instructions leads to the creation of faster production
rules for accomplishing tasks. Specifically, repeated
practice results in the compilation of task-specific
production rules that bypass declarative knowledge.
Because these rules are faster — due to bypassing
declarative memory and possibly being less complex —
they are more strongly rewarded and more likely to
bypass the retrieval of instructions in the future. As a
result, metacognitive performance becomes quicker,
less effortful, and more automatic.
The expert stage involves a robust accumulation of
production rules that have been refined and stored in
procedural memory. These productions can be deployed
automatically to act out monitoring and control
processes quickly and effectively. These productions
may be faster and less complex, resulting in a lower
metacognitive threshold and an improved perception of
affective experience. Metacognitive performance in this
case demonstrates many characteristics of expertise,
i.e., being fast, effective, automatic, and requiring
minimal working memory.
Deactivating meta-emotions
Proceduralization, the development of task-specific
production rules, assists in providing a computational
account of how training to perceive affective variations
(equanimity) results in the deactivation of meta-
emotions.
Recall that production rules match and fire off the
content of working memory at a default rate of 50ms.
That is, productions require at least 50ms to detect a
pattern held within working memory. Should a pattern
be perceived as sufficiently stable for over 50ms,
productions will automatically match and fire off that
pattern. Hence, the timing of production rules may be
considered a condition of the metacognitive threshold
(and psychophysical thresholds more generally) as it
provides a partial account of which properties of the
stimulus are needed to evoke a response, i.e., strength
of signal and perceived stability.
An analogous psychophysical threshold is well
known in vision research, where a light that flickers
rapidly enough appears to be constant (Landis, 1954).
This visual illusion is exploited in film production,
where still frames are sped up to 24 frames per second
to give images the appearance of consistency. The
visual threshold at which still images appear to be
constant has been referred to as the “moment of
fusion”. This visual threshold can be partially raised or
lowered due to individual differences such as fatigue
and age. For our purposes, the illusion of the flicker-
fusion phenomena is comparable to the illusion of
affective stability, in that they both rely on a person’s
inability to perceive change above a certain rate.
Similar to the visual threshold, an individual’s
metacognitive threshold is variable and can be lowered
through attention training to perceive weaker signals
from internal cognitive states, such as subtle changes in
affect. Proceduralization offers a mechanism for
developing and refining production rules that are more
sensitive to internal signals, so as to eventually break
the illusion of affective consistency.
A key insight into precisely how the refined
perception of affective change (equanimity) deactivates
emotional reactivity comes from the timing of
production rules.

Above the threshold
To the extent that a person’s metacognitive threshold is
above the 50ms firing rate of production rules, they will
perceive any pattern within working memory to be
relatively stable. Should a negative emotion appear to
be consistent over the 50ms threshold, productions have
sufficient time to match and fire a secondary negative
emotion in response to the first. Assuming the same
conditions, the secondary negative emotion may be
perceived and reacted to again, producing a tertiary
negative emotion. As long as the metacognitive
threshold remains, along with the illusion of affective
consistency, production rules may fire automatically,
and emotional reactivity may repeat indefinitely.
This exp la na ti on sheds light on a potential
mechanism that generates the continuous increase in
negative emotions as experienced within many
psychological disorders. Increasing and persistent
cycles of maladaptive emotions are among the most
common symptoms of mental illnesses and are
associated with Cognitive Attentional Syndrome (CAS;
Wells, 2009). A nearly universal phenomenon in
cognitive disorders, CAS is a style of negative
processing marked by fixed, negatively-biased attention
which causes maladaptive emotions to be preserved and
heightened, resulting in a continual state of emotional
distress.
While there is a lack of computational explanations
for the mechanisms underlying this style of maladaptive
processing, the timing of production rules can help
explain how negatively valenced emotions can be
heightened through a process of positive feedback.
Production rules also help explain the largely
unconscious and involuntary nature of emotional
reactions, underscoring the need for metacognitive
training to develop productions that counteract them.
Below the threshold
We propose that a key mechanism contributing to the
deactivation of meta-emotions is the ability to
perceive affective change below the 50ms firing rate of
production rules. Reducing the metacognitive threshold
below 50ms produces an effect similar to the visual
flicker-fusion illusion that occurs when the film speed
is reduced below 24 frames per second. The illusion of
consistency is broken, and one perceives the rapid
arising and passing of experience.
This refined perception of affective variations inhibits
production rules from matching to the constant
fluctuations in working memory (Figure 3). In effect,
production rules do not have enough time to identify the
rapidly changing pattern of affect. In principle, as long
as sufficient metacog nitive sensitivity remains,
productions are unable to fire secondary emotions.
Lowering one’s metacognitive threshold below the
50ms rate requires an expert level of metacognitive
skill, as it necessitates the accumulation of sufficiently
refined production rules. These expert production rules
are better able to detect subtle variations in affective
experience and fleeting signals from other internal
cognitive states. Conversely, if one’s metacognitive
threshold rises above 50ms, the affective pattern may
appear stable enough for emotional reactivity to
resume.
This account helps articulate how the subcomponents
of mindfulness training assist in diminishing cycles of
negative emotion in psychological disorders such as
Cognitive Attentional Syndrome. Individuals who
experience CAS are often caught in patterns of negative
emotion without a normal exit condition from the
informational loop (Wells, 2019). From a computational
standpoint, the development of production rules of the
type discussed would provide an exit condition from
maladaptive emotional loops that would otherwise
persist.
This ana ly si s highlights th e pivotal r ol e of
metacognitive training in emotional regulation and the
key mechanism by which metacognitive practices such
as detached mindfulness enhance the ability to perceive
emotions without reacting to them.
Figure 3. Above the 50ms threshold, an emotion is
perceived as sufficiently stable for productions to match
and fire secondary emotions. Below the 50ms threshold,
the perception of emotional impermanence prevents
productions from matching and firing secondary emotions.
Other considerations
Accounting for mindfulness with cognitive modeling is
a multifaceted endeavour, and there are many other
considerations. For example, there is the issue of buffer
decay, or how long patterns of activity can remain
within working memory. These issues would apply to
representations of both thought and emotion. Another
issue is the ability for productions to match to
emotional states and to declaratively label them. A
particular issue that arises here can be understood in
terms of partial matching, or the fidelity of the match. If
we take emotion to be a representation of neural activity
then we would expect it to have gradations of

variability. Since the ability to recognize emotions
would depend on our ability to match to these
representational gradients, we would need to assume
some form of fuzzy matching. This raises the possibility
that some individuals could have more finely tuned
productions and conceptual categories for matching
emotions, while others may have broader, more fuzzy
categories.
Finally, Conway-Smith and West (2023) argued that
the capacity of production rules to speed up could
increase one’s sensitivity to detecting shifts in
emotion, and discussed various ways that this speed up
could be modeled.
Conclusion
In this paper we have argued that Common Model type
architectures can account for important aspects of
mindfulness and meditation practices. In particular, we
ha ve em plo yed t he co nce pt of meta cogni tive
proceduralization to explore the mechanism by which
detached mindfulness disengages meta-emotions. A
complete model has yet to be constructed, as more
theoretical work is required to determine a method of
evaluation, considering there is presently no obvious
data source with which to compare. One future
possibility would be to better articulate the neural
correlates of this model and to compare these to the
neural imaging results of meditators.
By elucidating the computational processes involved
in detached mindfulness and its influence on emotional
reactivity, we contribute to a more comprehensive
computational understanding that integrates both
metacognitive monitoring and control within a unified
framework. Meditation on the impermanence of affect
is presently an edge case for the Common Model, one
that will likely raise questions as to its capacity to
simulate it. Our analysis demonstrates that the Common
Model framework is able to interpret this practice in a
way that accords with reports from practitioners, i.e., the
stages of learning, their experiences, and their ability to
apply it.
Moreov er, by applying th e ACT-R cognitive
architectu r e to t h e st u d y of m e t a c o g n itive
proceduralization, we help bridge the gap between
cognitive modeling and psychological practice. The
exploration of metacognitive proceduralization within
the framework of the Common Model, and specifically
ACT-R, offers a novel approach to understanding and
intervening in the cycle of negative emotional reactions.
Our approach facilitates the exploration of previously
underexamined facets of cognitive modeling, aiding in
the development of a more complete and integrated
cognitive architecture.
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